Systems and methods to use behavioral biometrics to detect and defend against a digital scam-in-progress

Abstract

Systems and methods for preventing fraud using behavioral biometrics may include a server with memory and a processor. The processor may be configured to create a behavioral biometric use-print for a user based on a plurality of observed and recorded user interactions and then monitor one or more behavioral biometrics of the user while the user accesses a user account. These monitored behavioral biometrics may be compared against the behavioral biometric use-print to determine if there are material deviations indicating that the user is under stress. When the server determines that the user is under stress, it may provide an intervention to help safeguard against potential in-process fraud.

We claim: 
     
         1 . A method for fraud prevention using behavioral biometrics, the method comprising the steps of:
 recording, by a processor, a plurality of behavioral biometrics of a user interacting with a user device;   generating, by the processor and based on the recorded plurality of behavioral biometrics, a behavioral biometric use-print for the user;   storing, in a database, the behavioral biometric use-print;   monitoring, by the processor, one or more behavioral biometrics of the user while the user accesses a user account on the user device;   applying a machine learning algorithm to the one or more behavioral biometrics of the user and the user's behavioral biometric use-print to determine that there is a material deviation in at least one of the one or more behavioral biometrics from the user's behavioral biometric use-print;   determine, by the processor and based on the material deviation, that the user is under stress;   providing, by the processor, an intervention to the user device based on the determination that the user is under stress; and   receiving user feedback on the intervention, whereby the user feedback is used as an input to the machine learning algorithm to train and refine the machine learning algorithm.   
     
     
         2 . The method of  claim 1 , wherein the user device is a smart phone with a mobile application associated with the user account. 
     
     
         3 . The method of  claim 1 , wherein the user device is a computer with a web browser capable of accessing a website associated with the user account. 
     
     
         4 . The method of  claim 1 , wherein the behavioral biometrics comprise at least one selected from a group of device holding preferences comprising, device orientation, screen focus, interaction gestures, screen interaction pressures, typing speed, typing error rates, typing habits including misspellings, browsing and interaction flow and order, scrolling speed, frequency, and cadence, click rates, frequency, and cadence, swipe speed, frequency, and cadence, jerkiness, stillness, and device elevation. 
     
     
         5 . The method of  claim 1 , wherein the behavioral biometric use-print for the user contains enough behavioral biometrics to uniquely identify the user. 
     
     
         6 . The method of  claim 1 , wherein the machine learning algorithm's determination of a material deviation is based on a degree of divergence from the user's behavioral biometric use-print and the monitored behavioral biometrics of the user while the user accesses a user account on the user device as well as a type of access attempted by the user in the user account. 
     
     
         7 . The method of  claim 6 , wherein the type of access attempted comprises one or more of attempting a password change, attempting an address change, attempting a funds transfer transaction over a threshold amount. 
     
     
         8 . The method of  claim 1 , wherein the intervention to the user device comprises a pop-up dialogue box window inquiring if the user's actions are currently being prompted by any third-party. 
     
     
         9 . The method of  claim 1 , wherein the intervention to the user device comprises a change to privileges in the user account. 
     
     
         10 . The method of  claim 9 , wherein the change to privileges in the user account comprises at least one selected from the group of requiring step-up authentication, reducing credit limits, lowering transfer amount maximums, and lowering cash advance maximums. 
     
     
         11 . A system for using behavioral biometrics to prevent digital scams, the system comprising:
 a memory storing a behavioral biometric use-print for a user; and   a processor configured to:
 record a plurality of behavioral biometrics of a user interacting with a user device; 
 generate, based on the recorded plurality of behavioral biometrics, the behavioral biometric use-print for the user; 
 monitor one or more behavioral biometrics of the user while the user accesses a user account on the user device; 
 apply a machine learning algorithm to the one or more behavioral biometrics of the user and the user's behavioral biometric use-print to determine that there is a material deviation in at least one of the one or more behavioral biometrics from the user's behavioral biometric use-print; 
 conclude, by the processor and based on the material deviation, that that the user is under stress; 
 provide, by the processor, an intervention to the user device based on the conclusion that the user is under stress; and 
 receive user feedback on the intervention, whereby the user feedback is used as an input to the machine learning algorithm to train and refine the machine learning algorithm. 
   
     
     
         12 . The system of  claim 11 , wherein the user device is a smart phone with a mobile application associated with the user account. 
     
     
         13 . The system of  claim 11 , wherein the user device is a computer with a web browser capable of accessing a website associated with the user account. 
     
     
         14 . The system of  claim 11 , wherein the behavioral biometrics comprise one or more of device holding preferences, device orientation, screen focus, interaction gestures, screen interaction pressures, typing speed, typing error rates, typing habits including misspellings, browsing and interaction flow and order, scrolling speed, frequency, and cadence, click rates, frequency, and cadence, swipe speed, frequency, and cadence, jerkiness, stillness, and device elevation. 
     
     
         15 . The system of  claim 11 , wherein the machine learning algorithm's determination of a material deviation is based on a degree of divergence from the user's behavioral biometric use-print and the monitored behavioral biometrics of the user while the user accesses a user account on the user device as well as a type of access attempted by the user in the user account. 
     
     
         16 . The system of  claim 15 , wherein the type of access attempted comprises one or more of attempting a password change, attempting an address change, attempting a funds transfer transaction over a threshold amount. 
     
     
         17 . The system of  claim 11 , wherein the intervention to the user device comprises a pop-up dialogue box window inquiring if the user is working in conjunction with any other person. 
     
     
         18 . The system of  claim 11 , wherein the intervention to the user device comprises a change to privileges in the user account. 
     
     
         19 . The system of  claim 18 , wherein the change to privileges in the user account comprises one or more of requiring step-up authentication, reducing credit limits, lowering transfer amount maximums, and lowering cash advance maximums. 
     
     
         20 . A computer-readable non-transitory medium comprising computer-executable instructions that, when executed by at least one processor, perform procedures comprising the steps of:
 recording a plurality of behavioral biometrics of a user interacting with a user device;   generating, based on the recorded plurality of behavioral biometrics, a behavioral biometric use-print for the user;   monitoring one or more behavioral biometrics of the user while the user accesses a user account on the user device;   applying a machine learning algorithm to the one or more behavioral biometrics of the user and the user's behavioral biometric use-print to determine that there is a material deviation in at least one of the one or more behavioral biometrics from the user's behavioral biometric use-print;   concluding, by the processor and based on the material deviation, that that the user is under stress;   providing, by the processor, an intervention to the user device based on the conclusion that the user is under stress; and   receiving user feedback on the intervention, whereby the user feedback is used as an input to the machine learning algorithm to train and refine the machine learning algorithm.